Welcome to the official repository for IT4123: Agent Based Computing practicals and assignments. This repository is maintained for uploading practical sessions and coursework related to the IT4123 module. All tasks are implemented using Python in Jupyter Notebooks and are intended to teach agent-based modeling, simulation concepts, and how to apply them to real problems.

📘 Course Module Details

• Course Code: IT4123
• Course Title: Agent Based Computing – Practicals
• University: University of Vavuniya
• Academic Year: 2025
• Student ID: 2020/ICT/108

⚙️ How to Use This Repository

1. Clone the Repository

To get started, clone this repository using Git:

git clone https://github.com/IT4123-Agent-Based-Computing-Practicals

2. Navigate to the Specific Folder

Once cloned, navigate to the folder for the specific practical or assignment:

cd IT4123-Agent-Based-Computing-Practicals

3. Follow the Instructions

Each folder contains a README.md file with setup instructions and task details. Follow them carefully to run the Jupyter Notebook files.

🗂️ Assignments

Assignments and practical sessions will be uploaded periodically. Each task will have a dedicated folder containing:

• Jupyter Notebook files (.ipynb)
• Setup instructions
• Sample datasets (if applicable)
• Solutions or expected outputs (where provided)

💻 Requirements

Before working on the practicals, ensure you have the following tools installed:

• Anaconda Distribution: Includes Python and Jupyter Notebook. Download from Anaconda's official site.
• Jupyter Notebook: Launch via Anaconda Navigator or terminal using jupyter notebook.
• Python 3.x: Used for scripting and simulation.
• Recommended Python libraries (install with pip or conda):
  • mesa (agent-based modeling framework)
  • numpy
  • pandas
  • matplotlib
  • seaborn
  • networkx
  • scipy
  • jupyterlab or notebook

Install example (conda):

conda create -n it4123 python=3.10
conda activate it4123
conda install -c conda-forge jupyterlab numpy pandas matplotlib seaborn networkx scipy
pip install mesa

Or using pip:

python -m venv it4123-venv
source it4123-venv/bin/activate  # on Windows: it4123-venv\Scripts\activate
pip install jupyterlab numpy pandas matplotlib seaborn networkx scipy mesa

🧪 How to Run Jupyter Notebooks

1. Launch Jupyter Notebook / JupyterLab

Open Anaconda Navigator and launch JupyterLab/Notebook, or run:

jupyter lab
# or
jupyter notebook

2. Open the Notebook

Navigate to the desired .ipynb file in your browser and open it.

3. Run the Cells

Execute each cell using Shift + Enter and follow the instructions provided in the notebook. Many notebooks include configuration cells at the top — run these first to install or import dependencies.

Tips:

• If a notebook depends on a dataset in the same folder, make sure the dataset file is present.
• If notebooks use relative imports from a local module, launch Jupyter from the repository root.

📚 Recommended Workflow

• Fork or clone the repository.
• Create a branch for each assignment or feature (e.g., feature/assignment-01).
• Commit notebooks and any additional scripts or datasets to the appropriate folder.
• Keep large datasets in a separate data/ folder and avoid committing unnecessarily large files to git.

📜 License

This repository is created for educational purposes for the IT4123 module. All content is intended for teaching and learning. Please check with your instructor or institution for any specific usage rights or licensing requirements.